# ML-Papers-Explained

**Repository Path**: airwolf0992/ML-Papers-Explained

## Basic Information

- **Project Name**: ML-Papers-Explained
- **Description**: 该项目来自GitHub 旨在介绍机器学习领域重点技术的研究论文，既有经典重现，也有最新前沿跟进，突出论文的主要创新点，讨论他们对研究领域的影响及其应用空间。
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-01-19
- **Last Updated**: 2023-01-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ML Papers Explained

Explanations to key concepts in ML

## Full Index

- [BART](https://www.kaggle.com/discussions/general/374254)
- [BEiT](https://www.kaggle.com/discussions/general/371762)
- [BERT](https://www.kaggle.com/code/abhinand05/bert-for-humans-tutorial-baseline)
- [ConvMixer](https://www.kaggle.com/discussions/general/339146)
- [DistilBERT](https://dair.ai/posts/TL;DR_DistillBERT/)
- [DiT](https://www.kaggle.com/discussions/general/369149)
- [Donut](https://www.kaggle.com/discussions/general/374249)
- [Fast RCNN](https://www.kaggle.com/discussions/general/342959)
- [Faster RCNN](https://www.kaggle.com/discussions/general/342969)
- [Feature Pyramid Network](https://www.kaggle.com/discussions/general/371783)
- [Focal Loss (RetinaNet)](https://www.kaggle.com/discussions/general/371924)
- [Layout LM](https://www.kaggle.com/discussions/general/335699)
- [Layout LM v2](https://www.kaggle.com/discussions/general/335865)
- [Layout LM v3](https://www.kaggle.com/discussions/general/377655)
- [LiLT](https://www.kaggle.com/discussions/general/376900)
- [Masked Autoencoder](https://www.kaggle.com/discussions/general/339141)
- [MobileBERT](https://dair.ai/posts/Summary-MobileBERT/)
- [RCNN](https://www.kaggle.com/discussions/general/342956)
- [SentenceBERT](https://dair.ai/posts/TLDR_SentenceBERT/)
- [Swin Transformer](https://www.kaggle.com/discussions/general/374258)
- [TableNet](https://www.kaggle.com/discussions/general/338315)
- [TinyBERT](https://dair.ai/posts/TinyBERT-Size_does_matter,_but_how_you_train_it_can_be_more_important/)
- [Transformer](https://dair.ai/posts/attention-is-all-you-need/)
- [Vision Transformer](https://www.kaggle.com/discussions/general/338841)
- [XLNet](https://dair.ai/posts/XLNet_outperforms_BERT_on_several_NLP_Tasks/)

## By Category

### RCNNs
- [RCNN](https://www.kaggle.com/discussions/general/342956)
- [Fast RCNN](https://www.kaggle.com/discussions/general/342959)
- [Faster RCNN](https://www.kaggle.com/discussions/general/342969)

## Transformers
- [BERT](https://www.kaggle.com/code/abhinand05/bert-for-humans-tutorial-baseline)
- [DistilBERT](https://dair.ai/posts/TL;DR_DistillBERT/)
- [MobileBERT](https://dair.ai/posts/Summary-MobileBERT/)
- [SentenceBERT](https://dair.ai/posts/TLDR_SentenceBERT/)
- [TinyBERT](https://dair.ai/posts/TinyBERT-Size_does_matter,_but_how_you_train_it_can_be_more_important/)
- [Transformer](https://dair.ai/posts/attention-is-all-you-need/)
- [XLNet](https://dair.ai/posts/XLNet_outperforms_BERT_on_several_NLP_Tasks/)

### Layout Transformers
- [Layout LM](https://www.kaggle.com/discussions/general/335699)
- [Layout LM v2](https://www.kaggle.com/discussions/general/335865)
- [Layout LM v3](https://www.kaggle.com/discussions/general/377655)
- [LiLT](https://www.kaggle.com/discussions/general/376900)

### Document Information Processing
- [TableNet](https://www.kaggle.com/discussions/general/338315)
- [DiT](https://www.kaggle.com/discussions/general/369149)
- [Donut](https://www.kaggle.com/discussions/general/374249)

### Vision Transformers
- [BEiT](https://www.kaggle.com/discussions/general/371762)
- [Masked Autoencoder](https://www.kaggle.com/discussions/general/339141)
- [Swin Transformer](https://www.kaggle.com/discussions/general/374258)
- [Vision Transformer](https://www.kaggle.com/discussions/general/338841)


## Single Stage Object Detectors
- [Feature Pyramid Network](https://www.kaggle.com/discussions/general/371783)
- [Focal Loss (RetinaNet)](https://www.kaggle.com/discussions/general/371924)

---
Reach out on [Twitter](https://twitter.com/RitvikRastogi19) if you have any questions.

If you are interested to contribute, feel free to open a PR.